CN110414088B - Space fuzzy evaluation method for suitability of birds-involved habitat by combining hydrodynamic model - Google Patents

Space fuzzy evaluation method for suitability of birds-involved habitat by combining hydrodynamic model Download PDF

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CN110414088B
CN110414088B CN201910619073.1A CN201910619073A CN110414088B CN 110414088 B CN110414088 B CN 110414088B CN 201910619073 A CN201910619073 A CN 201910619073A CN 110414088 B CN110414088 B CN 110414088B
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杨中华
朱政涛
程明村
槐文信
郑俊杰
李达
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Abstract

The invention relates to a space fuzzy evaluation method for suitability of a poultry-involved habitat by combining a hydrodynamic model, which comprises the following steps: selecting key habitat factors of a research area; establishing an evaluation grade set of key habitat factors; establishing a hydrodynamic model of a research area; establishing a piecewise semi-normal distribution membership function by adopting a spatial fuzzy evaluation method, determining the membership degree of the habitat factors according to the calculation result of the hydrodynamic model, and establishing a single-factor fuzzy evaluation matrix of each habitat factor; determining the relative importance degree of each habitat factor in a bird-involved ecological suitability evaluation system by adopting an analytic hierarchy process, giving different weight values to different indexes, and finally establishing dynamic evaluation models of the habitat suitability under different time-space conditions by combining a single-factor fuzzy evaluation matrix and each factor weight coefficient according to a research area hydrodynamic model. The invention comprehensively considers the influence of the water depth difference under different time-space conditions on the habitat suitability, and the evaluation on the habitat suitability is more accurate.

Description

Space fuzzy evaluation method for suitability of birds-involved habitat by combining hydrodynamic model
Technical Field
The invention relates to the technical field of environmental hydraulics, in particular to a spatial fuzzy evaluation method for suitability of a poultry-involved habitat by combining a hydrodynamic model.
Background
In the past, when the suitability of the poultry-involved habitat is evaluated, water depth is generally selected as an important evaluation factor, unified water level data is adopted for the water level of the whole habitat, and the water depth is calculated by combining topographic data to evaluate the suitability of the habitat. However, in practical situations, the water body is flowing, the water level has a certain specific drop, and the water level is not at the same level. Taking the suitability evaluation of the Poyang lake overwintering white crane habitat as an example, the water head difference of the Poyang lake abundant water year north and south lake surface reaches 0.96m, the water head difference of the dry water year reaches 1.64m, and the influence of the water head difference on the proper water depth of the white crane is great for 20-50 cm. The water depth is an important factor influencing the quality of the habitat, the accuracy of the water depth greatly influences the evaluation result of the habitat suitability, so the water depth is the most important index for evaluating the poultry-related habitat suitability, and in practice, the water depth of the habitat is greatly different along with different seasons, different time and different positions of the habitat, so the problem of comprehensively considering the influence of the water depth on the habitat suitability is urgently needed to be solved. In addition, other factors may also have a greater impact on the suitability of a habitat, such as the frequency of human activities. At present, the influence of a certain factor is only singly considered in the evaluation of the suitability of most of poultry-involved habitats, and obviously, the methods have great limitation and inaccurate evaluation structure.
Disclosure of Invention
The invention aims to provide a space fuzzy evaluation method for the suitability of a poultry-involved habitat by combining a hydrodynamic model, which comprehensively considers the difference of water depths under different time-space conditions and the influence of different water depths on the suitability of the habitat, so that the evaluation on the suitability of the habitat is more accurate and close to the actual condition.
The scheme adopted by the invention for solving the technical problems is as follows:
a fuzzy evaluation method for poultry-involved habitat suitability space combined with a hydrodynamic model dynamically evaluates habitat suitability of a research area under different space-time conditions based on a calculation result of the hydrodynamic model, and comprises the following steps:
s1: carrying out basic condition investigation on the involved poultry in a research area, selecting the concerned involved poultry, selecting key habitat factors from the suitability factors of the key habitat of the involved poultry, and constructing a key habitat factor set;
s2: establishing an evaluation grade set of the key habitat factors of the target involved poultry according to related research data;
s3: collecting basic data of hydrology, topography and meteorology of a research area, establishing a two-dimensional shallow water dynamic model according to the collected basic data, and inputting a calculation result of the two-dimensional shallow water dynamic model as a dynamic factor of a poultry-involved habitat suitability evaluation model and carrying out habitat suitability evaluation;
s4: establishing a piecewise semi-normal distribution membership function by adopting a space fuzzy evaluation method according to a hydrodynamic modelDetermining membership r of key habitat factor by calculation resultijAnd constructing a single-factor fuzzy evaluation matrix R of each key habitat factor according to the calculated membership degree of the key habitat factork，
Figure GDA0003002886980000021
Wherein, i is 1,.. multidot.n, n represents the total number of key habitat suitability evaluation factors, and j is 1,. multidot.m, m represents the total number of evaluation grades;
s5: determining the relative importance degree of each key habitat factor in the ecological suitability evaluation system of the wadding birds by adopting an analytic hierarchy process to obtain a weight coefficient vector W ═ W of a key habitat factor set1,w2,w3…wn]And a uniform value V ═ V is given to each evaluation grade set index value according to habitat suitability from poor to good1,v2,...,vm]Wherein v is1Is the lowest score, vmFinally, according to a hydrodynamic model of a research area, combining a single-factor fuzzy evaluation matrix, weight coefficient vectors of each key habitat factor and a unified score vector of habitat suitability, finally establishing a dynamic evaluation model B of the suitability of the habitat of the birds under different time-space conditions,
Figure GDA0003002886980000031
wherein, Bi＝W·RkiV, i is the grid cell number, W is the weight coefficient vector, RkiIs a fuzzy evaluation matrix of unit grids numbered i, V is a uniform score vector of habitat suitability, DiThe area of the cell grid numbered i, and n is the total number of cell grids in the study area.
Further, the step S1 specifically includes:
a. investigating basic conditions of the involved poultry: according to the current situation and historical data of the wading birds in the research area, the types of the wading birds, the distribution situation of the wading birds in the research area and the change rule of the wading bird population and quantity, the wading birds which are focused in the research area are determined;
b. constructing an evaluation index system: aiming at the researched birds, the key habitat factors of the target birds are selected according to the actual situation of the suitability of the birds in the research area, and the evaluation factors are constructed into a new key habitat factor set.
Further, the key habitat factors include habitat water depth, food resource availability, and human activity.
Further, the step S2 specifically includes:
1) obtaining the quantitative response relation between the target fowl-involved habitat suitability and key habitat factor index values according to the existing related research results;
2) establishing an evaluation level set P of the ith key habitat factor according to the habitat suitability and the response relation of key habitat factor index valuesi＝{Pi1,Pi2,...,PimIn the formula Pij(j 1.. times, m) is an index value corresponding to different levels of the ith influence factor, m represents that the evaluation index is divided into m levels, and Pi1,Pi2,...,PimBetween PminAnd PmaxWherein P ismaxRepresents the optimum value of the suitability of the corresponding habitat of the grade, PminIndicating that the scale corresponds to the worst habitat suitability.
Further, an evaluation grade set P of the ith habitat factor is establishedi＝{Pi1,Pi2,...,PimIn the time of the method, four basic cases are considered according to the habitat suitability and the response relation curve of the key influence factor index value:
the response relation curve is a monotone decreasing curve, and an ith habitat factor evaluation grade set P is constructedi＝{Pi1,Pi2,...,Pim}，Pmin＝Pi1＞Pi2＞...＞Pim＝Pmax；
II, constructing an ith habitat factor evaluation grade set P by taking the response relation curve as a monotone increasing curvei＝{Pi1,Pi2,...,Pim}，Pmin＝Pi1＜Pi2＜...＜Pim＝Pmax；
III, dividing the ith habitat factor evaluation level into an increasing set and a decreasing set according to the increasing curve and the decreasing curve of the response relation curve, respectively corresponding to habitat factor index values in a curve monotone increasing interval and a curve monotone decreasing interval, and increasing the set Pi Increase＝{Pi1,Pi2,...,Pim}，Pmin1＝Pi1＜Pi2＜...＜Pim＝PmaxSubtracting set Pi Reducing＝{Pi1,Pi2,...,Pim}，Pmin2＝Pi1＞Pi2＞...＞Pim＝Pmax；
IV, dividing the ith habitat factor evaluation grade into a decreasing set and an increasing set according to the curve of the response relation, wherein the curve of the response relation is a curve which decreases firstly and then increases secondly, the decreasing set P corresponds to the habitat factor index values of a curve monotone decreasing interval and a curve monotone increasing interval respectivelyi Reducing＝{Pi1,Pi2,...,Pim}，Pmin＝Pi1＞Pi2＞...＞Pim＝Pmax1Increasing set Pi Increase＝{Pi1,Pi2,...,Pim}，Pmin＝Pi1＜Pi2＜...＜Pim＝Pmax2。
Further, the step S3 specifically includes:
dividing a research area by using a triangular non-structural grid, establishing a two-dimensional shallow water hydrodynamic model by taking a two-dimensional shallow water equation set and a Godunov finite volume method as a frame according to collected basic data of hydrology, topography and meteorology of the research area, simulating water flow characteristic change of the research area by taking an actually measured flow water level as a model inlet and outlet condition, calculating water flow conditions of grid units according to the two-dimensional shallow water hydrodynamic model, and taking the calculated water flow conditions as dynamic input factors of a habitat suitability model and carrying out habitat suitability evaluation when a time step length exceeds a habitat evaluation period;
wherein, the control equation of the two-dimensional shallow water hydrodynamic model comprises:
A. two-dimensional shallow water equation set:
Figure GDA0003002886980000051
S＝Sb+Sf+Sw+Sc
Figure GDA0003002886980000052
B. godunov type finite volume format under triangular mesh:
Figure GDA0003002886980000053
wherein U represents a conservative amount; t represents time in units of s; x and y are Cartesian coordinate systems and the unit is m; F. g represents the numerical flux in the x and y directions respectively; s represents a source item, wherein SbRepresenting a topographic source item, SfIndicates the source item of friction resistance, SwRepresenting the wind stress source term, ScRepresenting the Coriolis force term generated by the rotation of the earth; h is water depth, and the unit is m; u and v are water flow velocity in x and y directions respectively, and the unit is m/s; g is gravity acceleration, and is 9.8m/s2，Sx、SyRespectively representing components of various source items in x and y directions; dividing a research area by adopting a triangular mesh, wherein i represents a mesh unit number, j represents a number of a boundary in a certain unit, and in the triangular mesh, j is 1,2 and 3; Δ t represents a time step in units of s; diRepresents the area of a cell of a triangular mesh, in m2；LijThe length of the jth boundary in the ith triangular mesh is expressed in m; fij、GijRespectively representing the x and y direction flux vectors of the jth boundary in the ith grid; theta denotes the angle between the corresponding boundary outer normal direction and the x positive direction, and is expressed by degrees.
Further, the step S4 specifically includes:
determining evaluation grades of different key habitat factors by adopting semi-normal distributionDegree of membership of, membership function formula according to evaluation level Pi＝{Pi1,Pi2,...,PimThe difference is divided into two cases:
when P is presentij＜Pi(j+1)The method comprises the following steps:
formula of membership function
Figure GDA0003002886980000061
Formula of membership function
Figure GDA0003002886980000062
Formula of membership function
Figure GDA0003002886980000063
When P is presentij＞Pi(j+1)The method comprises the following steps:
formula of membership function
Figure GDA0003002886980000064
Formula of membership function
Figure GDA0003002886980000065
Formula of membership function
Figure GDA0003002886980000066
Wherein i ═ 1., n, n represent the total number of habitat suitability evaluation factors; j 1.. m, m represents the total number of evaluation levels; ckiRepresenting a key habitat factor u at an arbitrary position k in spaceiA hydrodynamic model calculation value of (a);
obtaining the key habitat factor u at the space position k according to the formulaiAfter the spatial membership degree is obtained, a single-factor fuzzy evaluation matrix R for constructing key habitat factors is added in the hydrodynamic modelk；
Figure GDA0003002886980000071
Further, the step S5 specifically includes:
1) determining each key habitat factor { u ] by chromatography1,u2,...,unThe weight of key habitat factors are compared pairwise to determine the relative weight aijAij represents the scale of the importance comparison result of the evaluation factor i and the evaluation factor j, and can be determined according to the research result of the predecessor or the opinion of consulting experts, and a judgment matrix A is constructed according to the resultn：
An＝(aij)n×nWherein a isii＝1，aij＞0，aji＝1/aij；
2) After the judgment matrix passes consistency check, obtaining a corresponding eigenvector of the maximum eigenvalue of the matrix, normalizing the eigenvector to obtain a weight coefficient vector W [ W ] of each key habitat factor1,w2,w3,…,wn]Wherein w isi(ii) weight of influence of the ith key habitat factor on habitat suitability;
3) giving a uniform score V ═ V { V } from poor to good to each grade index value according to habitat suitability1,v2,...,vmIn which v is1Is the lowest score, vmThe highest score;
4) weighting coefficient vector W of each key habitat factor and single-factor fuzzy evaluation matrix R of single grid unit in hydrodynamic modelkiMultiplying the habitat suitability score vectors V to obtain a score B of the suitability evaluation of the single gridding poultry-involved habitati，
Figure GDA0003002886980000081
Wherein i is a grid unit number, n represents the total number of habitat suitability evaluation factors, and m represents the total number of evaluation levels;
5) taking the grid area of the unit of the research area as weight, and scoring B for the single grid unitiAfter weighted average, the birds inhabiting in the whole research area are obtainedOverall evaluation score for suitability for destination B:
Figure GDA0003002886980000082
wherein i is a cell grid number, DiIs the area of the unit grid numbered i, and n is the number of the unit grids.
Further, still include: under the hydrological boundary condition of the given hydrodynamic model, the method is adopted to calculate the total time length of the time length exceeding the hydrodynamic model simulation, so that the dynamic response relation between the suitability of the target poultry-involved habitat and the hydrological boundary condition of the research area is obtained, and the change process of the habitat suitability comprehensive evaluation score in the simulation period is obtained.
Compared with the prior art, the invention has at least the following beneficial effects:
(1) establishing a two-dimensional shallow water hydrodynamic model by taking a two-dimensional shallow water equation set and a Godunov finite volume method as a frame, simulating the characteristic change of water flow in a research area by taking an actually measured flow water level as an inlet and outlet condition of the model, and then calculating the obtained water depth according to the two-dimensional shallow water hydrodynamic model to be taken as a habitat evaluation index, wherein the obtained water depth is closer to the actual water depth, so that the evaluation result by taking the water depth as the evaluation index is more accurate, and the physical significance of the evaluation index is more sufficient;
(2) the invention adopts the triangular non-structural mesh to subdivide the research area, dynamically evaluates the habitat suitability of the research area under different time-space conditions based on the calculation result of the two-dimensional shallow water hydrodynamic model, comprehensively considers the influence of the water depth change in different mesh units under different time on the habitat suitability, meanwhile, a corresponding single-factor fuzzy evaluation matrix, a weight coefficient of each key habitat factor and a unified score of habitat suitability are established according to the response relation between the water depth of the habitat and the availability of food resources and the response relation between the habitat suitability and human activities, thereby obtaining a comprehensive evaluation score of the habitat suitability of the whole research area, so that the suitability of the habitat evaluated by the invention is more accurate, the method plays a more meaningful guiding role in the whole research area, and simultaneously overcomes the limitation that the influence of a certain factor is only singly considered in the prior art;
(3) the method provided by the invention can realize all processes by using a computer software technology, couple the spatial fuzzy evaluation method into the hydrodynamic model, and automatically obtain the habitat suitability distribution result of the research area only by changing the hydrological boundary conditions of the research area.
Detailed Description
The following examples are provided to further illustrate the present invention for better understanding, but the present invention is not limited to the following examples.
Selecting Yanghu wetland as a research area, and evaluating the suitability of the habitats of the wadded birds in different typical years in the research area, wherein the spatial fuzzy evaluation method for the suitability of the habitats of the wadded birds combined with the hydrodynamic model comprises the following steps:
s1: and carrying out basic condition investigation on the involved poultry in the research area, selecting the mainly concerned involved poultry, selecting key habitat factors from habitat suitability factors of the involved poultry, and constructing an influence factor set.
Nearly 95% of white cranes, 60% of swan gooses and 50% of white-pillow cranes all over the world live through the winter in the Poyang lake, so that the Poyang lake is the largest and most important habitat of migratory birds in the middle and downstream of Yangtze river or even in east Asia. Among nearly hundreds of species of overwintering migratory birds, the white crane has the strictest requirement on the environment. Therefore, the research on the biological characteristics of the white cranes living through the Poyang lake and the response of the white cranes to the change of the Poyang lake water depth has great significance for enhancing construction and protection of the ecological environment of the yang lake.
The evaluation indexes of the suitability of the wild animal habitat mainly comprise three types: in the present embodiment, water depth, food resource availability, and human activities are selected as key habitat factors, and an influence factor set W ═ water depth, food resource availability, and human activities are constructed in combination with studies on the distribution relationship between foraging, habitat activity range, and habitat of white crane.
S2: and establishing an evaluation grade set of the key habitat factors of the target involved poultry through related research data.
According to the existing related research results, the response curve of habitat suitability and water depth is increased and then decreased, and the response curve of habitat suitability and food resource availability is monotonically increased, so that the response relationship between the food resource availability and the water depth is indirectly obtained. In addition, the habitat suitability and the response relation of human activities can be obtained by a ray method, the habitat grade of each grid is obtained, and different habitat grades are evaluated according to the evaluation index of the frequency degree of the human activities, namely the higher the habitat grade is, the higher the score is, and the less the human activities are.
Based on the above mentioned response relationship between habitat suitability and influence factor index value, we can establish the evaluation level set P of the ith key habitat factori＝{Pi1,Pi2,...,PimIn the formula Pij(j 1.. times, m) is an index value corresponding to different levels of the ith influence factor, m represents that the evaluation index is divided into m levels, and Pi1,Pi2,...,PimBetween PminAnd PmaxWherein P ismaxRepresents the optimum value of the suitability of the corresponding habitat of the grade, PminIndicating that the scale corresponds to the worst habitat suitability.
As is known from the foregoing, the response curve of habitat suitability versus key influencer index values takes various forms, and therefore, here we can consider the response curve divided into four basic cases:
when the response relation curve is a monotone decreasing curve, an ith habitat factor evaluation grade set P is constructedi＝{Pi1,Pi2,...,PimIn which P ismin＝Pi1＞Pi2＞...＞Pim＝Pmax；
When the response relation curve is a monotone increasing curve, constructing an ith habitat factor evaluation grade set Pi＝{Pi1,Pi2,...,PimIn which P ismin＝Pi1＜Pi2＜...＜Pim＝Pmax；
When the response relation curve is an increasing curve and a decreasing curve, dividing the ith habitat factor evaluation grade into an increasing set and a decreasing set, wherein the increasing set and the decreasing set respectively correspond to habitat factor index values of a curve monotone increasing interval and a curve monotone decreasing interval, and the increasing set P isi Increase＝{Pi1,Pi2,...,Pim}，Pmin1＝Pi1＜Pi2＜...＜Pim＝PmaxSubtracting set Pi Reducing＝{Pi1,Pi2,...,Pim}，Pmin2＝Pi1＞Pi2＞...＞Pim＝Pmax；
When the response relation curve is a curve which decreases first and then increases, dividing the ith habitat factor evaluation grade into a decreasing set and an increasing set, wherein the decreasing set and the increasing set respectively correspond to habitat factor index values of a curve monotone decreasing interval and a curve monotone increasing interval, and the decreasing set Pi Reducing＝{Pi1,Pi2,...,Pim}，Pmin＝Pi1＞Pi2＞...＞Pim＝Pmax1Increasing set Pi Increase＝{Pi1,Pi2,...,Pim}，Pmin＝Pi1＜Pi2＜...＜Pim＝Pmax2。
From the foregoing, since the response curves of habitat suitability and water depth increase and decrease, we can determine the evaluation grade P first using the method in iii abovemin1,Pmin2,PmaxThen an augmented set P is obtained1 IncreaseAnd a reduced set P1 ReducingThen adopting equal spacing method to increase set P1 IncreaseAnd a reduced set P1 ReducingThe evaluation indexes were classified into 4 grades (i.e., m is 4). From the foregoing, it can be seen that the response curve of the food resource availability versus water depth is first increased and then decreased, and that P is first determinedmin1,Pmin2,PmaxThen, the increasing sets are collected by adopting an equal spacing method
Figure GDA0003002886980000111
Sum and subtract set
Figure GDA0003002886980000112
The evaluation indexes were classified into 4 grades. Finally, P is determined based on habitat suitability and response curve of human activitymin,PmaxThen, P is formed by the equal spacing method3The evaluation indexes were classified into 4 grades.
S3: collecting basic data of hydrology, terrain, meteorology and the like in a research area, and establishing a hydrodynamic model of the research area, wherein a control equation of the two-dimensional shallow water hydrodynamic model of the research area comprises the following steps:
A. two-dimensional shallow water equation set:
Figure GDA0003002886980000121
S＝Sb+Sf+Sw+Sc
Figure GDA0003002886980000122
B. godunov type finite volume format under triangular mesh:
Figure GDA0003002886980000123
wherein U represents a conservative amount; t represents time in units of s; x and y are Cartesian coordinate systems and the unit is m; F. g represents the numerical flux in the x and y directions respectively; s represents a source item, wherein SbRepresenting a topographic source item, SfIndicates the source item of friction resistance, SwRepresenting the wind stress source term, ScRepresenting the Coriolis force term generated by the rotation of the earth; h is water depth, and the unit is m; u and v are water flow velocity in x and y directions respectively, and the unit is m/s; g is gravity acceleration, and is 9.8m/s2，Sx、SyRespectively representing components of various source items in x and y directions; by usingA triangular mesh divides a research area, i represents a mesh unit number, j represents a number of a boundary in a certain unit, and in the triangular mesh, j is 1,2 and 3; Δ t represents a time step in units of s; diRepresents the area of a cell of a triangular mesh, in m2；LijThe length of the jth boundary in the ith triangular mesh is expressed in m; fij、GijRespectively representing the x and y direction flux vectors of the jth boundary in the ith grid; theta denotes the angle between the corresponding boundary outer normal direction and the x positive direction, and is expressed by degrees.
After a control equation of the hydrodynamic model of the research area is established, subdividing the research area by using a triangular non-structural grid, giving basic parameters, model calculation parameters, initial calculation conditions, set simulation duration and corresponding hydrological and topographic boundary conditions of the import and export of the research area, wherein the basic parameters of the research area comprise basic data such as hydrological, topographic and meteorological data of the research area collected in advance, establishing a two-dimensional shallow water hydrodynamic model by using the two-dimensional shallow water equation set and a Godunov finite volume method as a frame, taking an actually measured flow water level as the import and export conditions of the hydrodynamic model during simulation calculation, and taking the water flow conditions obtained by calculation of the hydrodynamic model as dynamic input factors of the suitability model of the habitat of birds; the specific calculation method in the step is as follows:
(a) subdividing a calculation area by using a triangular non-structural mesh, giving basic parameters of the calculation area, model calculation parameters, initial calculation conditions, set simulation time length and corresponding hydrological and topographic boundary conditions of an inlet and an outlet of a research area;
(b) determining a proper time step according to the CFL stability condition, and ensuring the stability and time step of Godunov finite volume format two-dimensional hydrodynamic model calculation
Figure GDA0003002886980000131
(CcflRepresenting the Korong condition number, generally taking 0.8), and the time step is updated in real time along with the change of the water flow condition;
(c) three types of grid boundaries are defined according to the position of the grid: a fixed wall non-water passing boundary, a grid internal water passing boundary, an inlet and outlet hydrological boundary;
(d) calculating boundary flux by using HLLC format according to different grid boundary conditions, substituting the boundary flux into Godunov type finite volume calculation formula under triangular grid, and updating to obtain the conservative quantity of the next time step
Figure GDA0003002886980000132
(e) And (c) when the time step exceeds the habitat evaluation period (for example, the habitat suitability evaluation result is output once every day by taking 1 day as the period), carrying out the following steps, and otherwise, returning to the step (b) for recalculation until the time step exceeds the period.
S4: establishing a piecewise semi-normal distribution membership function by adopting a spatial fuzzy evaluation method, determining the membership degree of the key habitat factor according to the calculation result of the hydrodynamic model, and establishing a single-factor fuzzy evaluation matrix R of the key habitat factork。
i. In step S2, an evaluation grade set is determined according to the response curve relationship, and first, the water depth is taken as an example, and the membership r of the water depth factor to each evaluation grade is determined by adopting a semi-normal distributionimI represents the ith influence factor, m represents the evaluation index divided into m grades, and since the response curve of habitat suitability and water depth increases and then decreases, an increasing set P of the response curve is found out1 IncreaseAnd a reduced set P1 Reducing：
P1 Increase＝{P11,P12,P13,P14}＝{0.0453,0.1098,0.1742,0.2386}(Pij＜Pi(j+1)Unit is m)
P1 Reducing＝{P11,P12,P13,P14}＝{0.4319,0.3675,0.3031,0.2386}(Pij＞Pi(j+1)Unit is m)
When the water depth h of the unit grid obtained by calculation of the hydrodynamic model is less than 0.2386m, an increasing set P is adopted1 IncreaseAs the evaluation grade set, when the water depth h of the unit grid calculated by the hydrodynamic model is larger than or equal to 0.2386mBy using a subtractive set P1 ReducingAs a set of evaluation ratings.
When P is adopted1 IncreaseWhen the {0.0453,0.1098,0.1742 and 0.2386} is used as the evaluation grade set, the membership degree r of the suitability and the water depth of each unit grid is calculated according to a hydrodynamic modelijAnd obtaining the following membership calculation formula:
if h is less than or equal to P110.0453m, the corresponding membership calculation formula is:
r11＝1,
Figure GDA0003002886980000151
if 0.0453m ═ P11＜h≤P120.1098m, the corresponding membership calculation formula is:
Figure GDA0003002886980000152
if 0.1098m ═ P12＜h≤P130.1742m, the corresponding membership calculation formula is:
Figure GDA0003002886980000153
if 0.1742m ═ P13＜h＜P140.2386m, the corresponding membership calculation formula is:
Figure GDA0003002886980000154
when P is adopted1 ReducingWhen the evaluation grades are set as {0.4319,0.3675,0.3031,0.2386}, the following membership calculation formula is obtained by the above hydrodynamic model calculation:
if h is greater than or equal to P110.4319m, the corresponding membership calculation formula is:
r11＝1,
Figure GDA0003002886980000155
if 0.3675m ═ P12≤h＜P110.4319m, the corresponding membership calculation formula is:
Figure GDA0003002886980000156
if 0.3031m ═ P13≤h＜P120.3675m, the corresponding membership calculation formula is:
Figure GDA0003002886980000157
if 0.2386m ═ P14≤h＜P130.3031m, the corresponding membership calculation formula is:
Figure GDA0003002886980000158
and ii, evaluating the food resource availability factor by adopting the same method as the method in the step i, wherein the quantity of the white crane foraging individuals and the winter bud biomass are in a linear relation, and the winter bud biomass is closely related to the water depth, so that the relation between the food resource availability and the water depth is indirectly obtained, wherein the relation is increased and then decreased. We determine the evaluation level of the food resource availability factor from the conclusion and formula of step S2 to find an increased set
Figure GDA0003002886980000161
Sum and subtract set
Figure GDA0003002886980000162
Figure GDA0003002886980000163
Figure GDA0003002886980000164
When the unit grid water depth h obtained by calculation of the hydrodynamic model is less than 1.242m, an increasing set P is adopted1 IncreaseAs an evaluation grade set, when the water depth h of the unit grid calculated by the hydrodynamic model is more than or equal to 1.242m, a subtraction set P is adopted1 ReducingAs a set of evaluation ratings.
When adopting
Figure GDA0003002886980000165
When the evaluation level set is used, the membership degree r of each unit grid suitability and water depth is calculated according to a hydrodynamic modelimAnd obtaining the following membership calculation formula:
if h is less than or equal to P211.122m, the corresponding membership calculation formula is:
r21＝1,
Figure GDA0003002886980000166
if 1.122m ═ P21＜h≤P221.162m, the corresponding membership calculation formula is:
Figure GDA0003002886980000167
if 1.162m ═ P22＜h≤P231.242m, the corresponding membership calculation formula is:
Figure GDA0003002886980000168
if 1.202m ═ P23＜h＜P241.242m, the corresponding membership calculation formula is:
Figure GDA0003002886980000169
when adopting
Figure GDA0003002886980000171
When the evaluation level set is used, the following membership calculation formula is obtained by using the hydrodynamic model:
if h is greater than or equal to P211.362m, the corresponding membership calculation formula is:
r21＝1,
Figure GDA0003002886980000172
if 1.322m ═ P12≤h＜P111.362m, the corresponding membership calculation formula is:
Figure GDA0003002886980000173
if 1.282m ═ P23≤h＜P221.322m, the corresponding membership calculation formula is:
Figure GDA0003002886980000174
if 1.242m ═ P24≤h＜P231.282m, the corresponding membership calculation formula is:
Figure GDA0003002886980000175
evaluating the human activity factor, and determining an evaluation grade according to the protection condition of the region where the computational unit grid is located:
Figure GDA0003002886980000176
(Pij＜Pi(j+1)，P31、P32、P33、P34corresponding to an unprotected area, a county-level natural protection area, a province-level natural protection area, a national wetland park and a Poyang lake national-level natural protection area in sequence). The conclusion made from the previous step S2And a formula according to the increasing set
Figure GDA0003002886980000177
The following membership calculation formula is obtained:
r31＝1,
Figure GDA0003002886980000178
after determining membership degrees of key habitat factors, namely { water depth, food resource availability and human activities } to each evaluation level, constructing a single-factor fuzzy evaluation matrix R of the key habitat factors in a hydrodynamic modelk：
Figure GDA0003002886980000181
S5: determining the relative importance degree of each key habitat factor in a bird-involved ecological suitability evaluation system by adopting an analytic hierarchy process, giving different weight values to different indexes, and finally establishing a dynamic evaluation model of the bird-involved habitat suitability under different time-space conditions by combining a single-factor fuzzy evaluation matrix, each factor weight coefficient vector and a habitat unified value vector according to a research area hydrodynamic model.
The method specifically comprises the following steps:
taking the Poyang lake overwintering white crane habitat suitability evaluation factor water depth as an example, giving the Poyang lake overwintering white crane habitat suitability evaluation factors including water depth, food resource availability and human activity intensity, and constructing a judgment matrix A by utilizing an analytic hierarchy processn。
The judgment matrix constructed by the hierarchical method has the following properties:
aij＝1/aji。
in table 1, when constructing the judgment matrix, the importance scale of each factor is obtained according to the importance degree of the judgment factor i and the factor j, and then the judgment matrix a is determinedn。
TABLE 1 evaluation factor importance Scale
Figure GDA0003002886980000182
Figure GDA0003002886980000191
According to the number n of the evaluation factors of the key habitat, determining a judgment matrix of the research as follows:
An：
Figure GDA0003002886980000192
② judgment matrix A3Maximum eigenvalue λmaxThe corresponding feature vector is W, for A3And (3) carrying out consistency evaluation, introducing a consistency index CI, a random consistency index RI and a check coefficient CR:
Figure GDA0003002886980000193
CR＝CI/RI
wherein, RI is a constant, and can be obtained by looking up a table according to the value of n, when CR is more than or equal to 0.1, the judgment matrix needs to be adjusted until CR is less than 0.1, at this moment, the judgment matrix is considered to meet the requirement of consistency check, the corresponding eigenvector of the maximum eigenvalue of the matrix is obtained, and after normalization processing is carried out on the eigenvector, the weight coefficient vector of each key habitat factor is obtained: w ═ W1,w2,w3]。
Third, the grade index value of each key habitat factor is endowed with a uniform value V ═ V { V ═ V from poor to good according to habitat suitability1,v2,v3,v4}＝{25,50,75,100}
Acquiring water flow conditions and position information of the ith grid unit of the research area through a hydrodynamic model; by grid depth of water information hiObtaining the membership r of the water depth factor and the food resource availability factor to each evaluation gradeij(ii) a Then, the habitat where the grids are located is judged by using a ray method through grid position informationObtaining the membership degree of the protection grade to each evaluation grade according to the current situation, thereby obtaining a single-factor fuzzy evaluation matrix R of the ith grid unitki(ii) a Finally, in the hydrodynamic model, a key habitat factor weight coefficient vector W and the ith grid unit single-factor fuzzy evaluation matrix RkiMultiplying the habitat suitability scoring vector V to obtain a score B of the suitability evaluation of the ith unit grid poultry-involved habitati：
Figure GDA0003002886980000201
[(w1r11+w2r21+w3r31)v1+(w1r12+w2r22+w3r32)v2+(w1r13+w2r23+w3r33)v3+(w1r14+w2r24+w3r34)v4]i
Using the area of the unit grid of the research area as the weight, and scoring the single grid BiAnd after weighted average, obtaining a comprehensive evaluation score B of the suitability of the birds-involved habitat in the whole research area:
Figure GDA0003002886980000202
wherein D isiN is the number of unit grids for the corresponding unit area;
repeating the steps S3-S5 until the calculation time length exceeds the total simulation time length of the hydrodynamic model under the condition of the hydrological boundary of the hydrodynamic model, thereby obtaining the dynamic response relation between the suitability of the target animal-involved habitat and the hydrological boundary condition of the research area and obtaining the change process of the comprehensive evaluation score of the habitat suitability during the simulation.
The basic data of the specific habitat are different, the evaluation content is different, and the person skilled in the art can input the actual situation into the model. The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made or substituted in a similar manner to the embodiments described herein by those skilled in the art without departing from the spirit of the invention or exceeding the scope thereof as defined in the appended claims.

Claims (9)

1. A spatial fuzzy evaluation method for suitability of poultry-involved habitat combined with a hydrodynamic model is characterized in that the evaluation method is used for dynamically evaluating the suitability of the habitat of a research area under different time-space conditions based on a calculation result of the hydrodynamic model, and comprises the following steps:
s1: carrying out basic condition investigation on the involved poultry in a research area, selecting the concerned involved poultry, selecting key habitat factors from the suitability factors of the key habitat of the involved poultry, and constructing a key habitat factor set;
s2: establishing an evaluation grade set of the key habitat factors of the target involved poultry according to related research data;
s3: collecting basic data of hydrology, topography and meteorology of a research area, establishing a two-dimensional shallow water dynamic model according to the collected basic data, and inputting a calculation result of the two-dimensional shallow water dynamic model as a dynamic factor of a poultry-involved habitat suitability evaluation model and carrying out habitat suitability evaluation;
s4: establishing a piecewise semi-normal distribution membership function by adopting a spatial fuzzy evaluation method, and determining the membership degree r of the key habitat factor according to the calculation result of the hydrodynamic modelijAnd constructing a single-factor fuzzy evaluation matrix R of each key habitat factor according to the calculated membership degree of the key habitat factork，
Figure FDA0002953087100000011
Wherein, i is 1,.. multidot.n, n represents the total number of key habitat suitability evaluation factors, and j is 1,. multidot.m, m represents the total number of evaluation grades;
s5: determining the relative importance degree of each key habitat factor in the evaluation system of the ecological suitability of the birds by adopting an analytic hierarchy process to obtain the key habitatWeight coefficient vector W of the set of informatics [ W ═ W%1,w2,w3…wn]And a uniform value V ═ V is given to each evaluation grade set index value according to habitat suitability from poor to good1,v2,...,vm]Wherein v is1Is the lowest score, vmFinally, according to a hydrodynamic model of a research area, combining a single-factor fuzzy evaluation matrix, weight coefficient vectors of each key habitat factor and a unified score vector of habitat suitability, finally establishing a dynamic evaluation model B of the suitability of the habitat of the birds under different time-space conditions,
Figure FDA0002953087100000021
wherein, Bi＝W·RkiV, i is the grid cell number, W is the weight coefficient vector, RkiIs a fuzzy evaluation matrix of unit grids numbered i, V is a uniform score vector of habitat suitability, DiThe area of the cell grid numbered i, and n is the total number of cell grids in the study area.
2. The spatial fuzzy evaluation method for suitability of the birds-in-war habitat according to claim 1, wherein the step S1 specifically comprises:
a. investigating basic conditions of the involved poultry: according to the current situation and historical data of the wading birds in the research area, the types of the wading birds, the distribution situation of the wading birds in the research area and the change rule of the wading bird population and quantity, the wading birds which are focused in the research area are determined;
b. constructing an evaluation index system: aiming at the researched birds, the key habitat factors of the target birds are selected according to the actual situation of the suitability of the birds in the research area, and the evaluation factors are constructed into a new key habitat factor set.
3. The spatial fuzzy evaluation method for suitability of poultry-involved habitat according to claim 2, wherein the key habitat factors include habitat water depth, food resource availability and human activities.
4. The spatial fuzzy evaluation method for suitability of the birds-in-war habitat according to claim 1, wherein the step S2 specifically comprises:
1) obtaining the quantitative response relation between the target fowl-involved habitat suitability and key habitat factor index values according to the existing related research results;
2) establishing an evaluation level set P of the ith key habitat factor according to the habitat suitability and the response relation of key habitat factor index valuesi＝{Pi1,Pi2,...,PimIn the formula, PijJ represents the j-th evaluation grade of the ith habitat factor, j is 1, …, m represents the evaluation index divided into m grades, Pi1,Pi2,...,PimBetween PminAnd PmaxWherein P ismaxRepresents the optimum value of the suitability of the corresponding habitat of the grade, PminIndicating that the scale corresponds to the worst habitat suitability.
5. The spatial fuzzy evaluation method for suitability of poultry-involved habitat according to claim 4, wherein the evaluation level set P of the ith habitat factor is establishedi＝{Pi1,Pi2,...,PimIn the time of the method, four basic cases are considered according to the habitat suitability and the response relation curve of the key influence factor index value:
the response relation curve is a monotone decreasing curve, and an ith habitat factor evaluation grade set P is constructedi＝{Pi1,Pi2,...,Pim}，Pmin＝Pi1＞Pi2＞...＞Pim＝Pmax；
II, constructing an ith habitat factor evaluation grade set P by taking the response relation curve as a monotone increasing curvei＝{Pi1,Pi2,...,Pim}，Pmin＝Pi1＜Pi2＜...＜Pim＝Pmax；
III, dividing the ith habitat factor evaluation level into an increasing set and a decreasing set according to the increasing curve and the decreasing curve of the response relation curve, respectively corresponding to habitat factor index values in a curve monotone increasing interval and a curve monotone decreasing interval, and increasing the set Pi Increase＝{Pi1,Pi2,...,Pim}，Pmin1＝Pi1＜Pi2＜...＜Pim＝PmaxSubtracting set Pi Reducing＝{Pi1,Pi2,...,Pim}，Pmin2＝Pi1＞Pi2＞...＞Pim＝Pmax；
IV, dividing the ith habitat factor evaluation grade into a decreasing set and an increasing set according to the curve of the response relation, wherein the curve of the response relation is a curve which decreases firstly and then increases secondly, the decreasing set P corresponds to the habitat factor index values of a curve monotone decreasing interval and a curve monotone increasing interval respectivelyi Reducing＝{Pi1,Pi2,...,Pim}，Pmin＝Pi1＞Pi2＞...＞Pim＝Pmax1Increasing set Pi Increase＝{Pi1,Pi2,...,Pim}，Pmin＝Pi1＜Pi2＜...＜Pim＝Pmax2。
6. The spatial fuzzy evaluation method for suitability of the birds-in-war habitat according to claim 1, wherein the step S3 specifically comprises:
dividing a research area by using a triangular non-structural grid, establishing a two-dimensional shallow water hydrodynamic model by taking a two-dimensional shallow water equation set and a Godunov finite volume method as a frame according to collected basic data of hydrology, topography and meteorology of the research area, simulating water flow characteristic change of the research area by taking an actually measured flow water level as a model inlet and outlet condition, calculating water flow conditions of grid units according to the two-dimensional shallow water hydrodynamic model, and taking the calculated water flow conditions as dynamic input factors of a habitat suitability model and carrying out habitat suitability evaluation when a time step length exceeds a habitat evaluation period;
wherein, the control equation of the two-dimensional shallow water hydrodynamic model comprises:
A. two-dimensional shallow water equation set:
Figure FDA0002953087100000041
S＝Sb+Sf+Sw+Sc
Figure FDA0002953087100000042
B. godunov type finite volume format under triangular mesh:
Figure FDA0002953087100000043
wherein U represents a conservative amount; t represents time in units of s; x and y are Cartesian coordinate systems and the unit is m; F. g represents the numerical flux in the x and y directions respectively; s represents a source item, wherein SbRepresenting a topographic source item, SfIndicates the source item of friction resistance, SwRepresenting the wind stress source term, ScRepresenting the Coriolis force term generated by the rotation of the earth; h is water depth, and the unit is m; u and v are water flow velocity in x and y directions respectively, and the unit is m/s; g is gravity acceleration, and is 9.8m/s2，Sx、SyRespectively representing components of various source items in x and y directions; dividing a research area by adopting a triangular mesh, wherein i represents a mesh unit number, j represents a number of a boundary in a certain unit, and in the triangular mesh, j is 1,2 and 3; Δ t represents a time step in units of s; diRepresents the area of a cell of a triangular mesh, in m2；LijThe length of the jth boundary in the ith triangular mesh is expressed in m; fij、GijRespectively representing the x and y direction flux vectors of the jth boundary in the ith grid; theta denotes outside the corresponding boundaryThe angle between the normal and the positive x direction is in degrees.
7. The spatial fuzzy evaluation method for suitability of the birds-in-war habitat according to claim 1, wherein the step S4 specifically comprises:
determining membership degrees of different key habitat factors to each evaluation grade by adopting semi-normal distribution, wherein a membership function formula is used for determining the membership degrees of different key habitat factors to each evaluation grade according to the evaluation grade Pi＝{Pi1,Pi2,...,PimThe difference is divided into two cases, in which, PijJ represents the j evaluation grade of the ith habitat factor, j is 1, …, m, m represents the evaluation index divided into m grades:
when P is presentij＜Pi(j+1)The method comprises the following steps:
formula of membership function
Figure FDA0002953087100000051
Formula of membership function
Figure FDA0002953087100000052
Formula of membership function
Figure FDA0002953087100000053
When P is presentij＞Pi(j+1)The method comprises the following steps:
formula of membership function
Figure FDA0002953087100000061
Formula of membership function
Figure FDA0002953087100000062
Formula of membership function
Figure FDA0002953087100000063
Wherein i ═ 1., n, n represent the total number of habitat suitability evaluation factors; j 1.. m, m represents the total number of evaluation levels; ckiRepresenting a key habitat factor u at an arbitrary position k in spaceiA hydrodynamic model calculation value of (a);
obtaining the key habitat factor u at the space position k according to the formulaiAfter the spatial membership degree is obtained, a single-factor fuzzy evaluation matrix R for constructing key habitat factors is added in the hydrodynamic modelk；
Figure FDA0002953087100000064
8. The spatial fuzzy evaluation method for suitability of the birds-in-war habitat according to claim 1, wherein the step S5 specifically comprises:
1) determining each key habitat factor { u ] by chromatography1,u2,...,unThe weight of key habitat factors are compared pairwise to determine the relative weight aij，aijThe scale representing the comparison result of the importance of the evaluation factor i and the evaluation factor j is determined according to the research result of the predecessor or the opinion of the consulting expert, and a judgment matrix A is constructed according to the scalen：
An＝(aij)n×nWherein a isii＝1，aij＞0，aji＝1/aij；
2) After the judgment matrix passes consistency check, obtaining a corresponding eigenvector of the maximum eigenvalue of the matrix, normalizing the eigenvector to obtain a weight coefficient vector W [ W ] of each key habitat factor1,w2,w3,...,wn]Wherein w isi(ii) weight of influence of the ith key habitat factor on habitat suitability;
3) giving a uniform score V ═ V { V } from poor to good to each grade index value according to habitat suitability1,v2,...,vmIn which v is1Is the lowest score, vmThe highest score;
4) weighting coefficient vector W of each key habitat factor and single-factor fuzzy evaluation matrix R of single grid unit in hydrodynamic modelkiMultiplying the habitat suitability score vectors V to obtain a score B of the suitability evaluation of the single gridding poultry-involved habitati，Wherein i is a grid unit number, n represents the total number of habitat suitability evaluation factors, and m represents the total number of evaluation levels;
5) taking the grid area of the unit of the research area as weight, and scoring B for the single grid unitiAnd after weighted average, obtaining a comprehensive evaluation score B of the suitability of the birds-involved habitat in the whole research area:
Figure FDA0002953087100000072
wherein i is a cell grid number, DiIs the area of the unit grid numbered i, and n is the number of the unit grids.
9. The spatial fuzzy evaluation method for suitability of the poultry-involved habitat according to claim 8, further comprising: and under the hydrological boundary condition of the given hydrodynamic model, repeating the steps S3-S5 until the calculation time exceeds the total hydrokinetic model simulation time, thereby obtaining the dynamic response relation between the suitability of the target poultry-involved habitat and the hydrological boundary condition of the research area, and obtaining the change process of the comprehensive evaluation score of the suitability of the habitat during the simulation.
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